Steely Hollow, OK
Steely Hollow wildfire risk explained
Steely Hollow's 253 buildings earn a 94th-percentile wildfire-risk score nationally under USFS's model — in USFS's highest wildfire-risk band nationally. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Steely Hollow's burn probability — fire likelihood with no building count factored in — sits at the 96th percentile nationally.
Where Steely Hollow's buildings actually sit
Of Steely Hollow's 253 counted buildings, 100% carry Direct exposure and only 0% carry Minimal — a lopsided split that puts defensible-space clearing ahead of vents or roofing as the intervention worth doing first.
How Steely Hollow compares
Inside Oklahoma, Steely Hollow sits at just the 69th percentile even though it scores 94th nationally — the state's overall wildfire exposure is high enough to make this a relatively quiet corner of it. Among the 31,521 US communities USFS scores, Steely Hollow ranks 2,024 for wildfire risk (1 is highest) and 23,054 by building count (1 is largest). Within Oklahoma alone, it ranks 260 of 834 places by risk. See the full county-by-county picture for Oklahoma on its state page.
Steely Hollow and the insurance market
Steely Hollow's 94th-percentile, very high rating is the kind of score behind a broader national pattern: insurers pulling back from the highest wildfire-risk markets, non-renewing or declining new policies outright, most visibly in California. Shopping around, not assuming automatic renewal, tends to matter here.
What would actually reduce this score
Because Direct exposure dominates in Steely Hollow (100%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Steely Hollow's figures come from
The methodology guide shows exactly how USFS turned 253 counted buildings into the percentiles shown above for Steely Hollow. The exposure-zones guide covers what Steely Hollow's dominant direct exposure actually means, with real examples from across the dataset.